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Software Testers May Soon be Replaced by AI Programs

But then some have to write these AL programs.it will create more job opportunities for programmers.

Artificial Intelligence (AI), has been transforming multiple sectors of industries and impacting several aspects of our daily life. Mostly AI has acted prominently in the fields of automating manual processes. And therefore, we will be investigating how AI has affected the realm of software testing, automated testing in particular.

Software testing is the process of assessing the performance of a program developed to check whether it is developed as per the client’s requirements and to find out whether there are faults and improve them before it is deemed ready for use.

Each time developers add new code, new tests must be carried out and it is the responsibility of the QAs to spend significant time on ensuring that new code does not break the existing codebase. Regression testing cycles are extremely time-consuming when undertaken manually and can burden QAs to a large extent.

Elon Musk Says He Could Make “Catgirl” Sex Robots If He Wanted To

To SpaceX and Tesla CEO Elon Musk, the future is teeming with humanoid robots.

During a lengthy and freewheeling conversation with TED’s Chris Anderson last week, Musk expanded on his vision of what it could look like to share everyday life with automatons doing our bidding.

And, predictably, the conversation jumped directly to the sexual implications of humanoid androids. Despite his busy personal schedule, Musk clearly does have some time to daydream.

Scientists create algorithm to assign a label to every pixel in the world, without human supervision

Labeling data can be a chore. It’s the main source of sustenance for computer-vision models; without it, they’d have a lot of difficulty identifying objects, people, and other important image characteristics. Yet producing just an hour of tagged and labeled data can take a whopping 800 hours of human time. Our high-fidelity understanding of the world develops as machines can better perceive and interact with our surroundings. But they need more help.

Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Microsoft, and Cornell University have attempted to solve this problem plaguing vision models by creating “STEGO,” an that can jointly discover and segment objects without any human labels at all, down to the pixel.

STEGO learns something called “semantic segmentation”—fancy speak for the process of assigning a label to every pixel in an image. Semantic segmentation is an important skill for today’s computer-vision systems because images can be cluttered with objects. Even more challenging is that these objects don’t always fit into literal boxes; algorithms tend to work better for discrete “things” like people and cars as opposed to “stuff” like vegetation, sky, and mashed potatoes. A previous system might simply perceive a nuanced scene of a dog playing in the park as just a dog, but by assigning every pixel of the image a label, STEGO can break the image into its main ingredients: a dog, sky, grass, and its owner.

Robotic Rat Climbs, Crawls, and Turns on a Dime

Alan DeRossettPutin propaganda is dividing opinions on Elon Musk for helping Ukraine and standing up to the Fossil fuel industry.

Walter LynsdaleI’m all for people making billions through technical advancement (teslas, space X rockets, the dojo chip are all pretty cool), but he comes out with a fair amount of double speak:

“people aren’t having enough babies” vs “we can make a humanoid robot”… See more.

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OpenAI DALL·E 2: Top 10 Insane Results! 🤖

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📝 The paper “Hierarchical Text-Conditional Image Generation with CLIP Latents” is available here:
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